Seismic Data Alignment via Cross-Migration Rescaling
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Solution Overview
Problem
Current seismic data comparison methods face challenges in aligning data from different processing methods, such as PSTM and PSDM, leading to difficulties in analyzing interactions and identifying optimal wellbore placement, as they produce mismatched three-dimensional grids, requiring re-processing which is time-consuming and costly.
Innovation Solution
Cross-migration rescaling and reorientation techniques are used to transition seismic models between different processing formats, allowing for comparison and analysis of seismic data from various vintages by rescaling, tilting, rotating, and translating 3D grids to align them in a common spatial frame, thereby maximizing the use of collected seismic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If seismic data from different processing methods (PSTM and PSDM) are directly compared, then analysis of subsurface geology can be performed, but the three-dimensional grids are mismatched leading to inaccurate results
Solution Approach 1:
The patent transforms seismic data by changing spatial parameters through rescaling, rotating, and translating operations. These parameter transformations align the three-dimensional grids from different processing methods (PSTM and PSDM) by adjusting their spatial coordinates and orientation, enabling accurate comparison while maintaining data integrity without requiring re-processing
Solution Approach 2:
The patent introduces an intermediary transformation process that acts as a mediator between mismatched seismic datasets. This intermediary step involves calculating transformation parameters and applying spatial adjustments to create a common reference frame, allowing data from different processing methods to be compared accurately without direct incompatibility
2Measurement precision
If seismic data is re-processed to align grids from different processing methods, then accurate subsurface analysis can be achieved, but the process is time-consuming and costly
Solution Approach 1:
The patent performs preliminary spatial transformation operations on seismic data before comparison or analysis. By pre-calculating and applying rescaling, rotating, and translating transformations to align grids in advance, the method eliminates the need for time-consuming re-processing later, achieving accurate wellbore placement analysis efficiently
Solution Approach 2:
The patent creates transformed copies of the original seismic datasets through spatial transformations. These copied and adjusted datasets maintain the essential geological information while being repositioned in a common coordinate system, allowing accurate comparison and analysis without modifying or re-processing the original expensive-to-acquire seismic data
3Loss of information
If multiple seismic data volumes from different vintages are collected, then a more complete picture of subsurface geology can be obtained, but the data cannot be effectively compared due to processing method differences
Solution Approach 1:
The patent creates a universal transformation framework that can handle multiple seismic data volumes from different vintages and processing methods. This multi-functional approach applies the same rescaling, rotating, and translating operations to various datasets, enabling them all to be compared in a common reference frame, thus preserving complete subsurface information while making it operationally comparable
Data Source
AI summary
A system and method can be used for to calibrating time-lapse seismic volumes by cross-migration rescaling and reorientation for use in determining optimal wellbore placement or production in a subsurface environment. Certain aspects include methods for cross-migration of data sets processed using different migration techniques. Pre-processing of the data sets, optimization of rescaling and reorientation, and identification of adjustment parameters associated with minimum global error can be used to achieve a time-dependent formation data set that addresses error in all input data sets.